Extracts the edges of a color image without converting it to grayscale.

Changes in color are detected even when the grayscale color of two pixels are the same. The edge strength is typically greater or equal to the magnitude obtained by simply filtering a grayscale image.

Optionally, the edge orientation can also be returned.

Example:
The image generated by the example code (presented here as the screenshot) shows two edge types:
White - edges found by both methods.
Red - edges found only by the color method.

This clearly shows that a significant amount of information is lost by the standard method, but it is recovered with the gradient method.

figure, im = imread('peppers.png'); imshow(im)

%get color edges and normalize magnitude
C = coloredges(im);
C = C / max(C(:));

%get grayscale edges and normalize magnitude
G_image = single(rgb2gray(im)) / 255;
G = sqrt(imfilter(G_image, fspecial('sobel')').^2 + imfilter(G_image, fspecial('sobel')).^2);
G = G / max(G(:));

%show comparison
figure, imshow(uint8(255 * cat(3, C, G, G)))

Algorithm:
The RGB color of each pixel is treated as a 3D vector, and the strength of the edge is the magnitude of the maximum gradient. This also works if the image is in any other (3-dimensional) color space. Direct formulas for the jacobian eigenvalues were used, so this function is vectorized and yields good results without sacrificing performance.